
import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  str	   =      (EulerDiscreteScheduler,)
 __lowerCAmelCase	:  Any	   =      10


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Any     		=   {
       """num_train_timesteps""": 11_00,
       """beta_start""": 0.0_0_0_1,
       """beta_end""": 0.0_2,
       """beta_schedule""": """linear""",
   }

   config.update(**SCREAMING_SNAKE_CASE_)
   return config


 def   lowercase__  (       self):


   '''simple docstring'''




   for timesteps in [10, 50, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,	[0.0_0_0_2, 0.0_0_2, 0.0_2]):
     self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,	beta_end=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for schedule in ["linear", "scaled_linear"]:
     self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   scheduler.set_timesteps(self.num_inference_steps)

   lowercase__	: Optional[Any]     		=   torch.manual_seed(0)

   lowercase__	: List[Any]     		=   self.dummy_model()
   lowercase__	: Optional[int]     		=   self.dummy_sample_deter * scheduler.init_noise_sigma
   lowercase__	: int     		=   sample.to(SCREAMING_SNAKE_CASE_)

   for i, t in enumerate(scheduler.timesteps):
     lowercase__	: Tuple     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: List[Any]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_)
     lowercase__	: str     		=   output.prev_sample

   lowercase__	: str     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: str     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
   assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config(prediction_type="""v_prediction""")
   lowercase__	: List[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   scheduler.set_timesteps(self.num_inference_steps)

   lowercase__	: Dict     		=   torch.manual_seed(0)

   lowercase__	: Union[str, Any]     		=   self.dummy_model()
   lowercase__	: List[str]     		=   self.dummy_sample_deter * scheduler.init_noise_sigma
   lowercase__	: Optional[int]     		=   sample.to(SCREAMING_SNAKE_CASE_)

   for i, t in enumerate(scheduler.timesteps):
     lowercase__	: Tuple     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: Union[str, Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: Optional[int]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_)
     lowercase__	: Any     		=   output.prev_sample

   lowercase__	: List[str]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: Tuple     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 0.0_0_0_2) < 1E-2
   assert abs(result_mean.item() - 2.26_76E-06) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.scheduler_classes[0]
   lowercase__	: Any     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   scheduler.set_timesteps(self.num_inference_steps       ,	device=SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   torch.manual_seed(0)

   lowercase__	: Dict     		=   self.dummy_model()
   lowercase__	: List[Any]     		=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   lowercase__	: List[str]     		=   sample.to(SCREAMING_SNAKE_CASE_)

   for t in scheduler.timesteps:
     lowercase__	: List[Any]     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: str     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: Any     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_)
     lowercase__	: List[Any]     		=   output.prev_sample

   lowercase__	: int     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: List[Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
   assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.scheduler_classes[0]
   lowercase__	: Any     		=   self.get_scheduler_config()
   lowercase__	: Optional[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_       ,	use_karras_sigmas=SCREAMING_SNAKE_CASE_)

   scheduler.set_timesteps(self.num_inference_steps       ,	device=SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   torch.manual_seed(0)

   lowercase__	: Any     		=   self.dummy_model()
   lowercase__	: Dict     		=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   lowercase__	: Any     		=   sample.to(SCREAMING_SNAKE_CASE_)

   for t in scheduler.timesteps:
     lowercase__	: Union[str, Any]     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: List[str]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     lowercase__	: int     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_)
     lowercase__	: Optional[Any]     		=   output.prev_sample

   lowercase__	: Any     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: List[Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1E-2
   assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1E-3







"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a				=logging.get_logger(__name__)

a				={
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  str    				=			'''deta'''
				A__ :  List[str]    				=			{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=900      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=6      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=8      ,					_lowerCamelCase=6      ,					_lowerCamelCase=1024      ,					_lowerCamelCase=8      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase="relu"      ,					_lowerCamelCase=256      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=1.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase="sine"      ,					_lowerCamelCase=5      ,					_lowerCamelCase=4      ,					_lowerCamelCase=4      ,					_lowerCamelCase=True      ,					_lowerCamelCase=300      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=1      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.2_5      ,					**_lowerCamelCase      ,					):
										if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
																lowerCamelCase__   =CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
										else:
																if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																						lowerCamelCase__   =backbone_config.pop("model_type" )
																						lowerCamelCase__   =CONFIG_MAPPING[backbone_model_type]
																						lowerCamelCase__   =config_class.from_dict(_lowerCamelCase )

										lowerCamelCase__   =backbone_config
										lowerCamelCase__   =num_queries
										lowerCamelCase__   =max_position_embeddings
										lowerCamelCase__   =d_model
										lowerCamelCase__   =encoder_ffn_dim
										lowerCamelCase__   =encoder_layers
										lowerCamelCase__   =encoder_attention_heads
										lowerCamelCase__   =decoder_ffn_dim
										lowerCamelCase__   =decoder_layers
										lowerCamelCase__   =decoder_attention_heads
										lowerCamelCase__   =dropout
										lowerCamelCase__   =attention_dropout
										lowerCamelCase__   =activation_dropout
										lowerCamelCase__   =activation_function
										lowerCamelCase__   =init_std
										lowerCamelCase__   =init_xavier_std
										lowerCamelCase__   =encoder_layerdrop
										lowerCamelCase__   =auxiliary_loss
										lowerCamelCase__   =position_embedding_type
										# deformable attributes
										lowerCamelCase__   =num_feature_levels
										lowerCamelCase__   =encoder_n_points
										lowerCamelCase__   =decoder_n_points
										lowerCamelCase__   =two_stage
										lowerCamelCase__   =two_stage_num_proposals
										lowerCamelCase__   =with_box_refine
										lowerCamelCase__   =assign_first_stage
										if two_stage is True and with_box_refine is False:
																raise ValueError("If two_stage is True, with_box_refine must be True." )
										# Hungarian matcher
										lowerCamelCase__   =class_cost
										lowerCamelCase__   =bbox_cost
										lowerCamelCase__   =giou_cost
										# Loss coefficients
										lowerCamelCase__   =mask_loss_coefficient
										lowerCamelCase__   =dice_loss_coefficient
										lowerCamelCase__   =bbox_loss_coefficient
										lowerCamelCase__   =giou_loss_coefficient
										lowerCamelCase__   =eos_coefficient
										lowerCamelCase__   =focal_alpha
										super().__init__(is_encoder_decoder=_lowerCamelCase      ,					**_lowerCamelCase )

				@property
				def 						_a      ( self ):
										return self.encoder_attention_heads

				@property
				def 						_a      ( self ):
										return self.d_model






				def 						_a      ( self ):
										lowerCamelCase__   =copy.deepcopy(self.__dict__ )
										lowerCamelCase__   =self.backbone_config.to_dict()
										lowerCamelCase__   =self.__class__.model_type
										return output


